Chicago Bulls Prospective
Player Analysis `19-20

Chicago Bulls NBA 2019-2020 Prospetive Player Report

Introduction:

Introduction

1

2

3

4

Player and Team Position Salary Trade Value Points/min
—————- ——– —— ———– ———-
Benny Pagett Cell 2
Cell 3 Cell 4
————- ————-
Mel Crunkhorn Cell 2
Cell 3 Cell 4


1. Introduction:

This section should provide relevant background information and justification for the project, including:

  1. relevant background information of basketball, including key metrics, position requirements etc

Key metrics: * Minutes played * Offensive value * Deffensive value (Rebounds) * Offensive value vs Defensive value * Assists * Points * Points per minute played * Rebounds * Offensive rebound % * Deffensive rebound % * Turnovers * Free Throw attempts * Free throw percentage * Attempts in the paint * Fouls * Regular season vs post season

** PER = Player efficiency rating *

From Wikipedia, the free encyclopedia Jump to navigationJump to search In basketball, effective field goal percentage (abbreviated eFG%) is a statistic that adjusts field goal percentage to account for the fact that three-point field goals count for three points while field goals only count for two points. [1] Its goal is to show what field goal percentage a two-point shooter would have to shoot at to match the output of a player who also shoots three-pointers. [2]

It is calculated by: \[ eFG(\%) =\frac{FG+(0.5*3P)}{FGA} \]

eDG% calculation

eDG% calculation





Total Rebounds/Minute

Total Rebounds/Minute

where:

FG = field goals made 3P = 3-point field goals made, FGA = field goal attempts, [3]

A rough approximation can also be had by:

\[ eFG(\%) =\frac{\frac{PPG-FT}{2}}{FGA} \]

where:

PPG = points per game FT = the free throws made FGA = field goal attempts The advantage of this second formula is that it highlights the aforementioned logic behind the statistic, where it is pretended that a player only shot two-point shots (hence the division of non-free-throw points by 2).

An additional formula that seems to be more in use by the statistics actually displayed on websites (but less cited by said websites) is: $$ eFG(%) =

$$

where:

2FG = 2-point field goals made 3FG = 3-point field goals made FGA = field goal attempts

2. Report scenario:

This report forms the tangible component of a reproducible data analysis project of a task given to the data analytics team by the Chicago Bulls GM . The task detailed the assessment of potential players to join/retain for the Chicago Bulls organisation for the 2019-20 NBA season.

The projected budget for player contracts for the 2019-20 season is $118 million dollars.

The aim of the project

This report and analysis aims to provide five starting players (PG, C, SG, PF & SF) of the highest value based upon a cost-benefit analysis. The purchase of the proposed athletes/players still allows sufficient budget to complete the remaining roster.

Justification and importance

The previous 2018-19 season saw the Chicago Bulls finish 27th out of 30 teams in the NBA (on win-loss record). The Chicago Bulls organisation has aspirations to rebuild their line-up and field a team with championship title potential for the upcoming 2019-20 season.

Note that you may choose a different order to present each of the elements listed above. ###



## 2. Reading and cleaning the raw data

This section should document the process used to read and clean the raw data. It should also include a description of the data sets used and variables in each. For brevity, you could provide a link to the specific variable descriptions, rather than writing these out in full within your report.



3. Exploratory analysis:

NBA player group

This section should document your exploratory data analysis and may include but is not limited to:

  1. checking for errors and missing values within the datasets

  2. checking the distribution of variables

  3. checking for relationships between variables, or differences between groups

  4. justification for decisions made about data modelling

Note that this section and the data cleaning section may be an iterative process, as you might find things about the data that need to be ‘cleaned up’ once you have explored the data further.



4. Data modelling and results:

This section may include but is not limited to:

  1. data modelling (e.g. creating a linear regression)

need to check source

term estimate std.error statistic p.value conf.low conf.high
(Intercept) -0.3821332 0.0180836 -21.1315032 0.0000000 -0.4177259 -0.3465405
eFGp 0.6985608 0.0314831 22.1884530 0.0000000 0.6365947 0.7605269
TRB_MP -0.0329833 0.0174981 -1.8849604 0.0604419 -0.0674237 0.0014572
Tm_use_total 2.3855803 0.0366744 65.0474831 0.0000000 2.3133963 2.4577642
EFF 0.0000096 0.0000043 2.2488204 0.0252797 0.0000012 0.0000181
TrV -0.0000080 0.0000104 -0.7740553 0.4395330 -0.0000284 0.0000124
term estimate std.error statistic p.value conf.low conf.high
(Intercept) -0.3821332 0.0180836 -21.1315032 0.0000000 -0.4177259 -0.3465405
eFGp 0.6985608 0.0314831 22.1884530 0.0000000 0.6365947 0.7605269
TRB_MP -0.0329833 0.0174981 -1.8849604 0.0604419 -0.0674237 0.0014572
Tm_use_total 2.3855803 0.0366744 65.0474831 0.0000000 2.3133963 2.4577642
EFF 0.0000096 0.0000043 2.2488204 0.0252797 0.0000012 0.0000181
TrV -0.0000080 0.0000104 -0.7740553 0.4395330 -0.0000284 0.0000124
[1] 0.483507

Predictive formula based off multiple regression model:

  • eFG = 0.55
  • TRB_MP = .2
  • Tm_use_total = 0.2
  • EFF = 1500
  • TrV = 600

\[ \beta_1 = -0.382 + 0.699 * 0.55 + -0.0330 * 0.2 + 2.39 * 0.20 + 0.00000965 * 1500 + -0.00000803 * 600 \]

  1. assumption checking

  2. model output and interpretation of your model


5. Player Analysis:


Points per/min vs Salary:



Player vs Salary analysis:




Trade Value vs Salary:






6. Player recommendations:

This section will be the key part that is presented to the general manager. Here you should present your recommendations for the best five starting players, but also think about what other important information they would want to know, and how it is best to present that information to them.



7. Summary:

Provide a brief summary which describes the key points and findings from your project. It will also be important to acknowledge any limitations of your model and overall approach to answering the question asked of you by the general manager.


  1. Reference List

Provide a reference list of any sources you used in the development of your report and justification of your arguments. Please use the Vancouver reference style (Links to an external site.) for the reference list and in-text references.

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If you find that you’re having to squint to read the text in your plot, you need to tweak fig.width. If fig.width is larger than the size the figure is rendered in the final doc, the text will be too small; if fig.width is smaller, the text will be too big

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##```{r Table1 test, echo=FALSE} knitr::kable((model_testing), select(player_name, Tm, Age, Pos, salary, TrV, EFF, Tm_use_total,PTS_per_MP, TRB_MP, exp_PTS_per_MP) %>% arrange(desc(exp_PTS_per_MP), salary) %>% top_n(20), caption = “Top 20 player selections.”) # initial table

References:

1.
Melo POS Vaz de, Almeida VAF, Loureiro AAF. Can complex network metrics predict the behavior of NBA teams? In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining. New York, NY, USA: Association for Computing Machinery, pp. 695–703.
2.
Xie Y, Dervieux C, Riederer E. R markdown cookbook.
3.
2018-19 NBA season summary.
4.
Fromal A. Understanding the NBA: Explaining advanced offensive stats and metrics.